Noise can be introduced to an image in many ways, such as during image transmission through a non-ideal channel, during image decoding, and during image encoding. The following are some examples of image noise that can occur. “Buttey” and “mosquito” noise are types of noise that result from Moving Picrare Experts Group (MPEG) compression. Using a limited bit precision during quantization in Wavelet compression, tiles, or macro-blocks in the case of MPEG, can become visible, lending a patchy appearance to the source image during decompression. Film grain noise is caused by the development process of silver chemical based film where emulsions aggregate into crystals causing what is commonly referred to as “salt and pepper” noise. Some film developers exploit his chemical process for a visual art-house effect.
Whatever the source of image noise, it typically has a usually displeasing effect on image quality. Even if the effect of noise such as film grain noise is desired, certain image processing tasks, such as format conversion, can render a tolerable level of visual noise in the image intolerable. For example, during format conversion it is often necessary to scale an incoming image, either to decrease or increase its size. The latter process can exacerbate the effect of noise. Filters that sharpen images are required to preserve detail such as edges and lines. However, such filters rend to worsen the visual noise in the image. It is desirable to be able to retain, and even enhance the detail in the image and, at the same time, suppress the noise. Traditional image processing techniques that rely on a non-adaptive, linear scheme are not able to overcome the image sharpening/noise reduction dichotomy, as they are conflicting requirements.
There are currently many methods for noise estimation and reduction. For example, in WO 95/24785 a noise floor is estimated using a sum of absolute differences. In WO 01/72032 A1 and WO 01/74056 a one N-dimensional transmission function is used to either pass through more or less of the pixel avoiding the need for multiple filters. Median filters are often cited as having good noise behaviour, especially edge enhancement. However, these filters typically do nor deal with correlated pixel detection and consequently they may make the wrong decision.
In “Space Scale Analysis for Image Sampling and Interpolation”, by G. E. Ford, R. R. Estes and H. Chew a method based on Anisotropic Diffusion is discussed. This method works by modulating the frequency response of a three-tap filter in either the horizontal direction or the vertical direction. The frequency response of the filter is a function of the step size of the centre pixel. It has drawbacks, as this method does not properly account for noise. The decision window is too small and the filter lengths are too short to obtain the required range of frequency responses needed for a high quality image interpolation of filtering. Further, there is a lack of information in the diagonal directions, and no steps are taken to distinguish between image detail and noise.
Other noise reduction algorithms are based on equalization techniques. Such techniques separate the frequency bands into many strata of image content frequency ranging from low to high. Wavelets, for example, have been used to decompose an image in this way. Noise reduction is then applied to the higher frequency layer, sometimes in the form of smoothing content of the high frequency layer. Recombining the layers results in an image that has less visible noise. However, there are at least two drawbacks to this approach. First, equalization usually requires separating data into different frequency bands, which is computationally expensive. Second, smoothing the high frequency layers not only reduces noise, but also details. For example, edges that are parts of fine detail such as a strand of hair can easily be interpreted as noise.
Thus, there is a need for a computationally inexpensive system and method for effectively reducing the noise in an image. It is an object of the present invention to obviate or mitigate at least some of the above mentioned disadvantages.